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Psychology Research and Applications
PRA > Volume 1, Number 2, June 2019

Can the Null Hypothesis Be “Proven” by Using Large Numbers of Predictor Variables?

Download PDF  (228.1 KB)PP. 35-40,  Pub. Date:June 21, 2019
DOI: 10.22606/pra.2019.12001

Author(s)
Michelle Perez, Walter R. Schumm, Abdullah AlRashed, Duane W. Crawford
Affiliation(s)
School of Family Studies and Human Services, College of Health and Human Sciences, Kansas State University, USA
School of Family Studies and Human Services, College of Health and Human Sciences, Kansas State University, USA
School of Family Studies and Human Services, College of Health and Human Sciences, Kansas State University, USA
School of Family Studies and Human Services, College of Health and Human Sciences, Kansas State University, USA
Abstract
Multivariate techniques have become commonplace in the social sciences. Some scholars have attempted to “prove” the null hypothesis by predicting one variable from a host of others. At least three risks or dangers accompany such attempts. First, the addition of multiple predictors may eventually render the apparent significance of any one independent variable to non-significance even if the underlying effect size remains medium to large. Second, the use of multiple predictors implies the potential for hundreds to millions of alternative models that could have been tested along with the models reported in an article. Third, ruling out the direct effects of a variable does not necessarily prove that the variable has no indirect effects on the outcome(s). Using a small data set of survey research in which truck drivers participated as respondents, we show how a theoretically and empirically strong relationship between income satisfaction and job satisfaction could be rendered non-significant (despite a medium or larger effect size) by adding enough other predictor variables, even if those additions did not make much theoretical sense. Thus, it is possible that other misinterpretations of the null hypothesis have occurred in the scientific literature when models with dozens of predictor variables have been used by authors who have had a goal of “proving” the null hypothesis.
Keywords
Truck drivers, income satisfaction, job satisfaction, methodology, indirect effects
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